{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    @Author: King\\n    @Date: 2019.06.20\\n    @Purpose: PageRank with DGL Message Passing\\n    @Introduction:   In this section we illustrate the usage of different levels of message passing API with PageRank on a small graph. In DGL, the message passing and feature transformations are all User-Defined Functions (UDFs).\\n    @Datasets: \\n    @Link : \\n    @Reference : https://docs.dgl.ai/tutorials/basics/3_pagerank.html\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    @Author: King\n",
    "    @Date: 2019.06.20\n",
    "    @Purpose: PageRank with DGL Message Passing\n",
    "    @Introduction:   The goal of this tutorial: to implement PageRank using DGL message passing interface.\n",
    "    @Datasets: \n",
    "    @Link : \n",
    "    @Reference : https://docs.dgl.ai/tutorials/basics/3_pagerank.html\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The PageRank Algorithm\n",
    "\n",
    "In each iteration of PageRank, every node (web page) first scatters its PageRank value uniformly to its downstream nodes. The new PageRank value of each node is computed by aggregating the received PageRank values from its neighbors, which is then adjusted by the damping factor:\n",
    "在PageRank的每次迭代中，每个节点（网页）首先将其PageRank值均匀地分散到其下游节点。通过聚合从其邻居接收的PageRank值来计算每个节点的新PageRank值，然后通过阻尼因子进行调整：\n",
    "\n",
    "$$\n",
    "P V(u)=\\frac{1-d}{N}+d \\times \\sum_{v \\in \\mathcal{N}(u)} \\frac{P V(v)}{D(v)}\n",
    "$$\n",
    "\n",
    "where N is the number of nodes in the graph; D(v) is the out-degree of a node v (节点 v 的超出程度); and N(u) is the neighbor nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A naive implementation\n",
    "\n",
    "Let us first create a graph with 100 nodes with NetworkX and convert it to a DGLGraph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import dgl\n",
    "\n",
    "N = 100  # number of nodes\n",
    "DAMP = 0.85  # damping factor\n",
    "K = 10  # number of iterations\n",
    "g = nx.nx.erdos_renyi_graph(N, 0.1)\n",
    "g = dgl.DGLGraph(g)\n",
    "nx.draw(g.to_networkx(), node_size=50, node_color=[[.5, .5, .5,]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the algorithm, PageRank consists of two phases in a typical scatter-gather pattern. We first initialize the PageRank value of each node to $\\frac{1}{N}$ and store each node’s out-degree as a node feature:\n",
    "\n",
    "根据该算法，PageRank由典型的分散 - 聚集模式中的两个阶段组成。\n",
    "我们首先将每个节点的PageRank值初始化为$\\frac{1}{N}$，并将每个节点的out-degree存储为节点特征："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "g.ndata['pv'] = torch.ones(N) / N\n",
    "g.ndata['deg'] = g.out_degrees(g.nodes()).float()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define the message function, which divides every node’s PageRank value by its out-degree(超出程度) and passes the result as message to its neighbors:\n",
    "\n",
    "然后我们定义消息函数，它将每个节点的PageRank值除以其out-degree，并将结果作为消息传递给它的邻居："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pagerank_message_func(edges):\n",
    "    return {'pv' : edges.src['pv'] / edges.src['deg']}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In DGL, the message functions are expressed as Edge UDFs. Edge UDFs take in a single argument edges. \n",
    "\n",
    "It has three members src, dst, and data for accessing source node features, destination node features, and edge features respectively. \n",
    "\n",
    "Here, the function computes messages only from source node features.\n",
    "\n",
    "Next, we define the reduce function, which removes and aggregates the messages from its mailbox, and computes its new PageRank value:\n",
    "接下来，我们定义reduce函数，它从mailbox中删除并聚合邮件，并计算其新的PageRank值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pagerank_reduce_func(nodes):\n",
    "    msgs = torch.sum(nodes.mailbox['pv'], dim=1)\n",
    "    pv = (1 - DAMP) / N + DAMP * msgs\n",
    "    return {'pv' : pv}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reduce functions are Node UDFs.\n",
    "\n",
    "Node UDFs have a single argument（论点） nodes, which has two members data and mailbox. data contains the node features while mailbox contains all incoming message features, stacked along the second dimension (hence the dim=1 argument).\n",
    "\n",
    "reduce函数是Node UDF。节点UDF具有单个参数节点，其具有两个成员数据和 mailbox。数据包含节点功能，而 mailbox 包含沿第二维堆叠的所有传入 mailbox 功能"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The message UDF works on a batch of edges, whereas the reduce UDF works on a batch of edges but outputs a batch of nodes. 消息UDF适用于一批边，而reduce UDF适用于一批边，但输出一批节点。\n",
    "\n",
    "\n",
    "Their relationships are as follows:\n",
    "\n",
    "![](img/UDF.png)\n",
    "\n",
    "We register the message function and reduce function, which will be called later by DGL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "g.register_message_func(pagerank_message_func)\n",
    "g.register_reduce_func(pagerank_reduce_func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The algorithm is then very straight-forward. Here is the code for one PageRank iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pagerank_naive(g):\n",
    "    # Phase #1: send out messages along all edges.\n",
    "    for u, v in zip(*g.edges()):\n",
    "        g.send((u, v))\n",
    "    # Phase #2: receive messages to compute new PageRank values.\n",
    "    for v in g.nodes():\n",
    "        g.recv(v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improvement with batching semantics\n",
    "\n",
    "The above code does not scale to large graph because it iterates over all the nodes. DGL solves this by letting user compute on a batch of nodes or edges. \n",
    "上面的代码不会扩展到大图，因为它遍历所有节点。DGL通过让用户在一批节点或边缘上进行计算来解决这个问题。\n",
    "\n",
    "\n",
    "For example, the following codes trigger message and reduce functions on multiple nodes and edges at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pagerank_batch(g):\n",
    "    g.send(g.edges())\n",
    "    g.recv(g.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we are still using the same reduce function pagerank_reduce_func, where nodes.mailbox['pv'] is a single tensor, stacking the incoming messages along the second dimension.请注意，我们仍在使用相同的reduce函数pagerank_reduce_func，其中nodes.mailbox ['pv']是单张量，沿第二维堆叠传入的消息。\n",
    "\n",
    "Naturally, one will wonder if this is even possible to perform reduce on all nodes in parallel, since each node may have different number of incoming messages and one cannot really “stack” tensors of different lengths together. \n",
    "当然，人们会想知道是否甚至可以并行地对所有节点执行 reduce，因为每个节点可能具有不同数量的传入消息，并且一个实际上不能将不同长度的张量“堆叠”在一起。\n",
    "\n",
    "In general, DGL solves the problem by grouping the nodes by the number of incoming messages, and calling the reduce function for each group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More improvement with higher level APIs\n",
    "\n",
    "DGL provides many routines that combines basic send and recv in various ways. They are called level-2 APIs. For example, the PageRank example can be further simplified as follows:\n",
    "\n",
    "DGL提供了许多以各种方式组合基本发送和recv的例程。它们被称为2级API。例如，PageRank示例可以进一步简化如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pagerank_level2(g):\n",
    "    g.update_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides update_all, we also have pull, push, and send_and_recv in this level-2 category. Please refer to the API reference for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Even more improvement with DGL builtin functions\n",
    "\n",
    "As some of the message and reduce functions are very commonly used, DGL also provides builtin functions. For example, two builtin functions can be used in the PageRank example.\n",
    "\n",
    "- dgl.function.copy_src(src, out) is an edge UDF that computes the output using the source node feature data. User needs to specify the name of the source feature data (src) and the output name (out).\n",
    "- dgl.function.sum(msg, out) is a node UDF that sums the messages in the node’s mailbox. User needs to specify the message name (msg) and the output name (out).\n",
    "\n",
    "For example, the PageRank example can be rewritten as following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dgl.function as fn\n",
    "\n",
    "def pagerank_builtin(g):\n",
    "    g.ndata['pv'] = g.ndata['pv'] / g.ndata['deg']\n",
    "    g.update_all(message_func=fn.copy_src(src='pv', out='m'),\n",
    "                 reduce_func=fn.sum(msg='m',out='m_sum'))\n",
    "    g.ndata['pv'] = (1 - DAMP) / N + DAMP * g.ndata['m_sum']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we directly provide the UDFs to the update_all as its arguments. This will override the previously registered UDFs.\n",
    "\n",
    "In addition to cleaner code, using builtin functions also gives DGL the opportunity to fuse operations together, resulting in faster execution. For example, DGL will fuse the copy_src message function and sum reduce function into one sparse matrix-vector (spMV) multiplication.\n",
    "\n",
    "This section describes why spMV can speed up the scatter-gather phase in PageRank. For more details about the builtin functions in DGL, please read the API reference.\n",
    "本节描述了为什么spMV可以加速PageRank中的分散 - 聚集阶段。有关DGL中内置函数的更多详细信息，请阅读API参考。\n",
    "\n",
    "You can also download and run the codes to feel the difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\progrom\\python\\python\\python3\\lib\\site-packages\\dgl\\base.py:18: UserWarning: Initializer is not set. Use zero initializer instead. To suppress this warning, use `set_initializer` to explicitly specify which initializer to use.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.0099, 0.0107, 0.0100, 0.0107, 0.0110, 0.0094, 0.0111, 0.0083, 0.0092,\n",
      "        0.0082, 0.0125, 0.0135, 0.0084, 0.0100, 0.0142, 0.0076, 0.0075, 0.0150,\n",
      "        0.0073, 0.0099, 0.0109, 0.0101, 0.0161, 0.0085, 0.0102, 0.0098, 0.0090,\n",
      "        0.0119, 0.0100, 0.0092, 0.0099, 0.0076, 0.0132, 0.0066, 0.0107, 0.0084,\n",
      "        0.0149, 0.0091, 0.0127, 0.0075, 0.0108, 0.0118, 0.0076, 0.0100, 0.0085,\n",
      "        0.0135, 0.0092, 0.0100, 0.0119, 0.0101, 0.0058, 0.0110, 0.0098, 0.0101,\n",
      "        0.0107, 0.0100, 0.0098, 0.0076, 0.0093, 0.0143, 0.0107, 0.0066, 0.0099,\n",
      "        0.0099, 0.0091, 0.0057, 0.0075, 0.0101, 0.0085, 0.0102, 0.0068, 0.0117,\n",
      "        0.0142, 0.0082, 0.0124, 0.0135, 0.0110, 0.0126, 0.0058, 0.0076, 0.0074,\n",
      "        0.0058, 0.0108, 0.0093, 0.0117, 0.0074, 0.0073, 0.0091, 0.0108, 0.0126,\n",
      "        0.0135, 0.0111, 0.0076, 0.0083, 0.0074, 0.0126, 0.0107, 0.0117, 0.0083,\n",
      "        0.0124])\n"
     ]
    }
   ],
   "source": [
    "for k in range(K):\n",
    "    # Uncomment the corresponding line to select different version.\n",
    "    # pagerank_naive(g)\n",
    "    # pagerank_batch(g)\n",
    "    # pagerank_level2(g)\n",
    "    pagerank_builtin(g)\n",
    "print(g.ndata['pv'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using spMV for PageRank\n",
    "\n",
    "Using builtin functions allows DGL to understand the semantics of UDFs and thus allows more efficient implementation for you. For example, in the case of PageRank, one common trick to accelerate it is using its linear algebra form.\n",
    "使用内置函数允许DGL理解UDF的语义，从而为您提供更高效的实现。例如，在PageRank的情况下，加速它的一个常见技巧是使用其线性代数形式。\n",
    "\n",
    "$$\n",
    "\\mathbf{R}^{k}=\\frac{1-d}{N} \\mathbf{1}+d \\mathbf{A} * \\mathbf{R}^{k-1}\n",
    "$$\n",
    "\n",
    "Here, $\\mathbf{R}^{k}$ is the vector of the PageRank values of all nodes at iteration k; A is the sparse adjacency matrix of the graph. \n",
    "\n",
    "Computing this equation is quite efficient because there exists efficient GPU kernel for the sparse-matrix-vector-multiplication (spMV). \n",
    "\n",
    "DGL detects whether such optimization is available through the builtin functions. If the certain combination of builtins can be mapped to a spMV kernel (e.g. the pagerank example), DGL will use it automatically. As a result, we recommend using builtin functions whenever it is possible.\n",
    "DGL通过内置函数检测是否可以进行此类优化。如果内置的某些组合可以映射到spMV内核（例如pagerank示例），DGL将自动使用它。因此，我们建议尽可能使用内置函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Next steps\n",
    "\n",
    "- Learn how to use DGL builtin functions to write more efficient message passing (link).\n",
    "- Check out the overview page of all the model tutorials.\n",
    "- Would like to know more about Graph Neural Networks? Start with the GCN tutorial.\n",
    "- Would like to know how DGL batches multiple graphs? Start with the TreeLSTM tutorial.\n",
    "- Would like to play with some graph generative models? Start with our tutorial on the Deep Generative Model of Graphs.\n",
    "- Would like to see how traditional models are interpreted in a view of graph? Check out our tutorials on CapsuleNet and Transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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